Data mining techniques for analysing data extracted from serious games: a systematic literature review

Abstract

Serious games are applications that pursue, on the one hand, the users' entertainment and, on the other hand, look to promote their learning, cognitive stimulation, among reaching other objectives. Moreover, data generated from those games (e.g., demographic information, gaming precision, user efficiency) provide insights helpful in improving certain aspects such as the attention and memory of the gamers. Therefore, applying data mining techniques over those data allows obtaining multiple patterns to improve the game interface, identify preferences, discover, predict, train, and stimulate the users' cognitive situation, among other aspects, to reach the games' objectives. Unfortunately, although several solutions have been addressed about this topic, no secondary studies have been found to condensate research that uses data mining to extract patterns from serious games. Thus, this paper presents a Systematic Literature Review (SLR) to extract such evidence from studies reported between 2001 and 2021. Besides, this SLR aims to answer research questions involving serious games solutions that train the cognitive functions of th